Performance-analysis-based Acceleration of Image Quality Assessment

Algorithms for image/video quality assessment (QA) aim to predict the qualitiesof images in a manner that agrees with subjective quality ratings. Over the lastseveral decades, the major impetus in QA research has focused on improving predictiveperformance; very few studies have focused on analyzing and improving theruntime performance of QA algorithms. Modern algorithms of image/video qualityassessment commonly employed two stages: (1) a local frequency-based decomposition, and (2) block-based statistical comparisons between the frequency coefficients of the reference and distorted images. These two stages constitute the bulk of the computation and runtime required for QA. This research thesis presents a performance analysis of and techniques for accelerating these stages. We also specifically analyze and accelerate one representative QA algorithm, Most Apparent Distortion (MAD), which was developed by Eric Larson and Damon Chandler in 2010 [1]. We identify the bottlenecks from the above-mentioned stages, and we present methods of acceleration using generalized integral image, inline expansion, a GPGPU implementation, and other code modifications. We show how a combination of these approaches can yield a speedup of 47x.The content of the report is divided into five different chapters. In Chapter 1,we present a general overview of QA algorithms, current work on improving the computational performance and execution time of QA algorithms, and an introduction toour work. In Chapter 2, we describe MAD algorithm, the first performance analysis,and the systems used to test the performance. In Chapter 3, we present generalizedintegral image and inline expansion techniques. In this chapter, we also providethe results of each technique in terms of speeding up running time. Chapter 4 providesGPGPU and some other code optimization techniques with the timing results.Finally, the conclusion are proposed in the Chapter 5 to summarize the report.Electrical Engineerin

Estudo comparativo de técnicas de análise de textura em imagens e aprendizagem de máquina para classificação de Phragmites australis usando imagens de alta resolução com cor no espectro do visível

TCC(graduação) - Universidade Federal de Santa Catarina. Campus Araranguá. Engenharia da Computação.Phragmites australis (common reed) comumente encontrada em zonas úmidas costeiras pode alterar rapidamente a ecologia por competir e superar as plantas nativas por espaço e pelos recursos. Além disso, este tipo de vegetação representa um perigo de navegação para embarcações menores, prejudicando a visibilidade ao longo do litoral e em torno de curvas e canais de rios. Os esforços de gerencialmento direcionados a plantas não nativas de Phragmites dependem fortemente de um mapeamento preciso das áreas invadidads. No entanto, o mapeamento de Phragmites representa um desafio único por diferentes razões. Identificar e mapear Phragmites pode ajudar os gerentes de recurso a restaurar zonas húmidas afetadas. Neste trabalho, quatro técnicas de extração de características foram testadas: gabor filters, grey level co-occurrence matrix, segmentation-based fractal texture analysis e wavelet texture analysis. Estes algoritmos foram combinados com três estruturas de rede neural artificial: multilayer perceptron, probabilistic neural network e radial basis function network. Além disso, objetivando reduzir o tempo computacional, uma implementação na Graphics Processing Unit do melhor método identificado foi realizada. O estudo de avaliação foi realizado com imagens adquiridas no delta de Pearl River localizado no sudeste da Louisiana e no sudoeste do Mississippi, Estados Unidos da América. Em comparação com os resultados apresentados no estado da arte, wavelet texture analysis com probabilistic neural network e segmentation-based fractal texture analysis com probabilistic neural network apresentaram melhorias em várias variáveis estatísticas como acurácia geral e o kappa. Além disso, o nível de Phragmites agreement aumentou considerávelmente. Nos mostramos que os erros de omissão e comissão restantes geralmente estão localizados ao longo dos limites das áreas identificadas como Phragmites, o que reduz os esforços desnecessários para os gerentes de recursos na busca de áreas inexistentes.Phragmites australis (common reed) commonly found in the coastal wetlands can rapidly alter the ecology by outcompeting with natives for space and resources. In addition, this type of vegetation presents a navigation hazard to smaller boats by impairing visibility along shorelines and around bends of canals and rivers. Management efforts targeting non-native Phragmites rely heavily on accurately mapping invaded areas. However, mapping Phragmites represents a unique challenge for different reasons. Identifying and mapping Phragmites can help resource managers to restore affected wetlands. In this work, four feature extraction methods were tested: gabor filters, grey level co-occurrence matrix, segmentation-based fractal texture analysis, and wavelet texture analysis. These algorithms were combined with three artificial neural network architectures: multilayer perceptron, probabilistic neural network, and radial basis function network. In addition, aiming to reduce the computational cost, a graphics processing unit implementation of the best result was performed. Evaluation study was conducted with imagery acquired in the delta of Pearl River located in southeastern Louisiana and southwestern Mississippi, United States of America. In comparison to state-of-art results, wavelet texture analysis with probabilistic neural network and segmentation-based fractal texture analysis with probabilistic neural network presented presented improvements in several statistical variables such as overall accuracy and kappa value. Furthermore, the Phragmites agreement increased considerably. We show that the remaining omission and commission errors are generally located along boundaries of patches with Phragmites, which reduces unnecessary efforts for resource managers while searching for nonexistent patches

Performance and Microarchitectural Analysis for Image Quality Assessment

This thesis presents performance analysis for five matured Image Quality Assessment algorithms: VSNR, MAD, MSSIM, BLIINDS, and VIF, using the VTune ... from Intel. The main performance parameter considered is execution time. First, we conduct Hotspot Analysis to find the most time consuming sections for the five algorithms. Second, we perform Microarchitecural Analysis to analyze the behavior of the algorithms for Intel's Sandy Bridge microarchitecture to find architectural bottlenecks. Existing research for improving the performance of IQA algorithms is based on advanced signal processing techniques. Our research focuses on the interaction of IQA algorithms with the underlying hardware and architectural resources. We propose techniques to improve performance using coding techniques that exploit the hardware resources and consequently improve the execution time and computational performance. Along with software tuning methods, we also propose a generic custom IQA hardware engine based on the microarchitectural analysis and the behavior of these five IQA algorithms with the underlying microarchitectural resources.School of Electrical & Computer Engineerin

Metalik yansımalı yüzeylerde otomatik çizik tespiti için görüntü işleme sistemi.

In industry, problems due to human error, mechanical flaws and transportation may occur; besides, they need to be detected in fast and efficient ways. In order to eliminate failure of human inspection, automated systems come in action, usually image processing involved. This thesis work, targets one common mass production problem on specular surfaces, i.e. scratch detection. To achieve this, we have implemented two different prototypes. The low-cost system is based on basic line detection, and the mid-end system depends on learning based detection. Both systems are implemented on embedded platforms and performance comparisons are done. Detailed analysis is carried out on computational cost and detection performance. This real-world episode is done on a mechanical prototype in laboratory environmentM.S. - Master of Scienc

Strategies for improving efficiency and efficacy of image quality assessment algorithms

Image quality assessment (IQA) research aims to predict the qualities of images in a manner that agrees with subjective quality ratings. Over the last several decades, the major impetus in IQA research has focused on improving prediction efficacy globally (across images) of distortion-specific types or general types; very few studies have explored local image quality (within images), or IQA algorithm for improved JPEG2000 coding. Even fewer studies have focused on analyzing and improving the runtime performance of IQA algorithms. Moreover, reduced-reference (RR) IQA is also a new field to be explored, when the transmitting bandwidth is limited, side information about original image was received with distorted image at the receiver. This report explored these four topics. For local image quality, we provided a local sharpness database, and we analyzed the database along with current sharpness metrics. We revealed that human highly agreed when rating sharpness of small blocks. Overall, this sharpness database is a true representation of human subjective ratings and current sharpness algorithms could reach 0.87 in terms of SROCC score. For JPEG2000 coding using IQA, we provided a new JPEG2000 image database, which includes only same total distortion images. Analysis of existing IQA algorithms on this database revealed that even though current algorithms perform reasonably well on JPEG2000-compressed images in popular image-quality databases, they often fail to predict the correct rankings on our database's images. Based on the framework of Most Apparent Distortion (MAD), a new algorithm, MADDWT is then proposed using local DWT coefficient statistics to predict the perceived distortion due to subband quantization. MADDWT outperforms all others algorithms on this database, and shows a promising use in JPEG2000 coding. For efficiency of IQA algorithms, this paper is the first to examine IQA algorithms from the perspective of their interaction with the underlying hardware and microarchitectural resources, and to perform a systematic performance analysis using state-of-the-art tools and techniques from other computing disciplines. We implemented four popular full-reference IQA algorithms and two no-reference algorithms in C++ based on the code provided by their respective authors. Hotspot analysis and microarchitectural analysis of each algorithm were performed and compared. Despite the fact that all six algorithms share common algorithmic operations (e.g., filterbanks and statistical computations), our results revealed that different IQA algorithms overwhelm different microarchitectural resources and give rise to different types of bottlenecks. For RR IQA, we also provide a new framework based on multiscale sharpness map. This framework employs multiscale sharpness maps as reduced information. As we will demonstrate, our framework with 2% reduced information can outperform other frameworks, which employ from 2% to 3% reduced information. Our framework is also competitive to current state-of-the-art FR algorithms

Mathematical Morphology for Quantification in Biological & Medical Image Analysis

Mathematical morphology is an established field of image processing first introduced as an application of set and lattice theories. Originally used to characterise particle distributions, mathematical morphology has gone on to be a core tool required for such important analysis methods as skeletonisation and the watershed transform. In this thesis, I introduce a selection of new image analysis techniques based on mathematical morphology. Utilising assumptions of shape, I propose a new approach for the enhancement of vessel-like objects in images: the bowler-hat transform. Built upon morphological operations, this approach is successful at challenges such as junctions and robust against noise. The bowler-hat transform is shown to give better results than competitor methods on challenging data such as retinal/fundus imagery. Building further on morphological operations, I introduce two novel methods for particle and blob detection. The first of which is developed in the context of colocalisation, a standard biological assay, and the second, which is based on Hilbert-Edge Detection And Ranging (HEDAR), with regard to nuclei detection and counting in fluorescent microscopy. These methods are shown to produce accurate and informative results for sub-pixel and supra-pixel object counting in complex and noisy biological scenarios. I propose a new approach for the automated extraction and measurement of object thickness for intricate and complicated vessels, such as brain vascular in medical images. This pipeline depends on two key technologies: semi-automated segmentation by advanced level-set methods and automatic thickness calculation based on morphological operations. This approach is validated and results demonstrating the broad range of challenges posed by these images and the possible limitations of this pipeline are shown. This thesis represents a significant contribution to the field of image processing using mathematical morphology and the methods within are transferable to a range of complex challenges present across biomedical image analysis